Field of the Invention
Embodiments of the present invention relate generally to computer science and, more specifically, to techniques for predicting perceptual video quality.
Description of the Related Art
Efficiently and accurately encoding source video is essential for real-time delivery of video content. After the encoded video content is received, the source video is decoded and viewed or otherwise operated upon. Some encoding processes employ lossless compression algorithms, such as Huffman coding, to enable exact replication of the source. By contrast, to increase compression rates and/or reduce the size of the encoded video content, other encoding processes leverage lossy data compression techniques that eliminate selected information, typically enabling only approximate reconstruction of the source. Further distortion may be introduced during resizing operations in which the video is scaled-up to a larger resolution to match the dimensions of a display device.
Manually verifying the quality of delivered video is prohibitively time consuming. Consequently, to ensure an acceptable video watching experience, efficiently and accurately predicting the quality of delivered video is desirable. Accordingly, automated video quality assessment is often an integral part of the encoding and streaming infrastructure—employed in a variety of processes such as evaluating encoders and fine-tune streaming bitrates to maintain video quality.
In one approach to assessing the quality of encoded videos, a full-reference quality metric, such as peak signal-to-noise ratio (PSNR), is used to compare the source video to the encoded video. However, while such metrics accurately reflect signal fidelity (i.e., the faithfulness of the encoded video to the source video), these metrics do not reliably predict human perception of quality. For example, fidelity measurements typically do not reflect that visual artifacts in still scenes are likely to noticeably degrade the viewing experience more than visual artifacts in fast-motion scenes. Further, due to such perceptual effects, such fidelity metrics are content-dependent and, therefore, inconsistent across different types of video data. For example, fidelity degradation in action movies that consist primarily of fast-motion scenes is less noticeable than fidelity degradation in slow-paced documentaries.
As the foregoing illustrates, what is needed in the art are more effective techniques for predicting the perceived quality of videos.